Internet-Guided Cognitive Behavioral Therapy for Insomnia Among Patients With Traumatic Brain Injury

Key Points Question What is the efficacy of fully automated internet-guided cognitive behavioral therapy for insomnia (eCBT-I) in military service members and veterans with traumatic brain injury (TBI) and moderate to severe insomnia? Findings In this randomized clinical trial that included 50 military service members and veterans who completed postintervention evaluation, the Insomnia Severity Index score decreased by 6.0 points in those randomized to eCBT-I vs 2.3 points in those randomized to sleep education. The extent of insomnia improvement correlated with depressive symptom improvement in the eCBT-I group. Meaning These findings suggest that when successfully completed, eCBT-I can provide clinical benefits in military service members and veterans with TBI and insomnia.

Participants who believed they had received the active intervention also reported greater decreases in insomnia than those who were uncertain or believed they had received the control intervention.No participants who were assigned to control believed that they had been assigned to the active intervention, though 5 participants assigned to active intervention believed that they had been assigned to control.The MIDAS is based on headaches over the previous 90 days, so only changes from baseline to the 3-month follow-up time point are presented.Furthermore, since the MIDAS questions 2 and 4 are often misinterpreted by participants, a modified MIDAS score consisting of the sum of responses to questions 1, 3, and 5 was analyzed.

eMethods. Statistical Analysis
Power Calculations: In addition to the power calculations used in the design of the study included in the protocol, we conducted subsequent power calculations using a simulation based on varying target sample sizes.We wanted to assess whether power was maintained even if target sample sizes were smaller than originally expected.These power calculations were based on: 1) original assumptions used in the original study design; 2) 3:1 treatment to control group allocation; 3) type I error rate=0.For the simulation results as they relate to this study (see figure below), we found that for mean differences in change in mean ISI between treatment arms > 3, that power of 0.8 was maintained for a range of sample sizes.
Once the SHUT-I data were collected, prior to the analysis of the study data, we conducted a power analysis given the actual data including the observed mean difference in ISI score for the parameter of interest, the observed variability in the data, and based on the actual observed underlying correlation in ISI score within subjects, using the same power calculation tool as used in the simulation.We assumed a Type I error=0.05, a two-sided test and available study N=106.This analysis indicated that the power based on the actual study data was 0.7.
Given the observed differences in covariate patterns in those missing data vs. those not missing data, we conducted sensitivity analyses to address the differences in attrition rates between the groups.Specifically, we carried out sensitivity analyses as outlined in Section 11.1 of the Statistical Analysis Plan (SAP), using inverse-probability of censoring weight (IPCW) estimation.We assessed the probability of participants missing at follow-up using a pooled logistic model to predict censoring at follow-up, using participants' demographic information available at the baseline visit.We calculated the predicted probability of participants not being censored and used the reciprocal of this to assign weights to any participant with observed data at the follow-up visits, i.e., the smaller the probability of not being censored at follow-up, for example, expected for a younger participant, the greater the weight assigned to a participant with observed data at follow-up of that particular age.We assumed censoring models for different sets of baseline predictors.The results of these sensitivity analyses indicate there were negligible differences in terms of inference between the original analysis and analyses that accounted for loss-to-follow based on baseline covariates that were found to differ significantly between those who were present at follow-up and those missing at follow-up.eReferences 1. Kuhn E, Miller KE, Puran D, et

eFigure 1 .
Expected Benefit Prior to Starting the Intervention vs Change in ISI From Baseline to Post Intervention eFigure 2. Believed Group Assignment vs Change in ISI From Baseline to Post Intervention eFigure 3. Participant Ratings of the Intervention vs Change in ISI From Baseline to Post Intervention eFigure 4. Interaction Between Sleep Medication Use and Group Assignment in Effects on ISI eFigure 5. As-Treated Analyses of Primary and Key Secondary Outcome Measures Involving Only Participants Who Completed All Online Modules and All Assessments eFigure 6. Correlations Between Changes in Self-Reported Insomnia and Changes in PTSD Symptom Severity, With Spearman ρ Values eFigure 7. Correlations Between Changes in Self-Reported Insomnia and Changes in Self-Reported Sleep Quality, With Spearman ρ Values eFigure 8. Correlations Between Changes in Self-Reported Insomnia and Changes in Self-Reported Fatigue Impact, With Spearman ρ Values eFigure 9. Correlations Between Changes in Self-Reported Insomnia and Changes in Migraine-Related Disability, With Spearman ρ Values eTable 1. Demographic Characteristics of Participants Who Completed Immediate Post Intervention Follow-Up Evaluations eTable 2. Demographic Characteristics of Participants Who Completed 3-Month Follow-Up Evaluations eTable 3. Baseline Clinical Scores for Participants Who Completed vs Were Missing Immediate Postintervention Follow-Up Evaluations eTable 4. Baseline Clinical Scores for Participants Who Completed vs Were Missing 3-Month Post-Intervention Follow-Up Evaluation eTable 5. Demographics for Participants Who Completed vs Were Missing Immediate Post-Intervention Follow-Up Evaluations eTable 6. Demographics for Participants Who Completed vs Were Missing at 3-Month Post Intervention Follow-Up Evaluations eMethods.Statistical Analysis eAppendix.Additional Limitations eReferences This supplementary material has been provided by the authors to give readers additional information about their work.eFigure 1. Expected Benefit Prior to Starting the Intervention vs Change in ISI From Baseline between participants with moderate vs. high expected benefit.eFigure 2. Believed Group Assignment vs Change in ISI From Baseline to Post Intervention

eFigure 3 .
Participant Ratings of the Intervention vs Change in ISI From Baseline to Post Intervention Efficacy, B. Perceived benefit, C. Usability, D. Likelihood of recommending the intervention to friends and family.eFigure 4. Interaction Between Sleep Medication Use and Group Assignment in Effects on ISI Self-reported insomnia severity, the primary outcome measure, B. Depression symptom severity, C. PTSD symptom severity, D. Self-reported sleep quality, E. Migraine-related disability, F. Fatigue impact.Data reported as a function of group (eCBT-I vs. education control) and assessment time point.Sample sizes were n=23 and n=12 for the eCBT-I and control groups respectively.Error bars indicate standard deviations.* indicates p<0.05 difference between groups.eFigure 6. Correlations Between Changes in Self-Reported Insomnia and Changes in PTSD Symptom Severity, With Spearman ρ Values eCBT-I r=0.42, p=0.024 eFigure 7. Correlations Between Changes in Self-Reported Insomnia and Changes in Self-Reported Sleep Quality, With Spearman ρ Values Correlations Between Changes in Self-Reported Insomnia and Changes in Self-Reported Fatigue Impact, With Spearman ρ Values 20, N.S.Increased FACIT scores represent improvement in fatigue impact.eFigure 9. Correlations Between Changes in Self-Reported Insomnia and Changes in Migraine-Related Disability, With Spearman ρ Values 05 and 2-sided test of the parameter of interest (i.e.difference in change from baseline to post-intervention in mean ISI score between the treatment and control groups); and 4) varying levels of within-subject correlation in ISI scores.Details as they relate the simulation are described in Fu et al. (2023); Power Simulation Program: An Adaptable Application for Assessment of Power in Planning and Pre-Data Analysis of Clinical Study Data-An MTBI 2 Study https://zenodo.org/records/8436456.
IPCW(1) included age only and IPCW(2) included age, military rank, and education level as different predictors of loss to follow-up.Distribution of weights are shown below for the different timepoints and across the different covariates found to be associated with missing at follow-up.Models for loss to follow-up: IPCW(1) included age only and IPCW(2) included age, military rank, and education level as different predictors of loss to follow-up.*Statistically significant effect in primary analysis measure.